In government and health care, predictive models enable professionals to serve the public more economically and effectively. For example, save paper by setting the office printer to the default of double-sided printing. Because people tend to ignore letters written in bureaucratese and fail to complete buggy computer forms, simplify the language and user interface. Doing so does not restrict choices; rather, options are arranged and presented in ways that help people make day-to-day choices that are consistent with their long-term goals. The story of a recent political campaign illustrates the idea.
The strategy was judicious: One might naively design a model to simply identify likely Obama voters. But doing so would waste resources and potentially annoy many supporters already intending to vote for Obama. At the opposite extreme, directing voters to the doors of hard-core Romney supporters would be counterproductive. The smart strategy was to identify those voters most likely to change their behavior if visited by a campaign worker. A predictive model can weigh more factors—and do so more consistently, accurately, and economically—than the unaided judgment of overstretched campaign workers.
But the story does not end here. The Obama campaign was distinctive in combining the use of predictive analytics with outreach tactics motivated by behavioral science. This tactic was motivated by psychological research indicating that people are more likely to follow through on actions that they have committed to.
Second, volunteers would also ask people to articulate a specific plan to vote, even down to the specific time of day they would go to the polls. This tactic reflected psychological research suggesting that forming even a simple plan increases the likelihood that people will follow through. Regardless of application, the implementation must be successful in two distinct senses: First, the model must be converted into a functioning piece of software that gathers and combines data elements and produces a useful prediction or indication with suitably short turnaround time.
In many cases, determining the appropriate action is, at least in principle, relatively straightforward. For example, if an analysis singles out a highly talented yet underpaid baseball player, scout him. If an actuarial model indicates that a policyholder is a risky driver, set his or her rates accordingly. If an emergency room triage model indicates a high risk of heart attack, send the patient to intensive care. But in many other situations, exemplified by the challenge of getting out the vote, a predictive model can at best point the end user in the right direction. It cannot suggest how to prompt the desired behavior change.
When the ultimate goal is behavior change, predictive analytics and the science of behavioral nudges can serve as two parts of a greater, more effective whole. Once one starts thinking along these lines, other promising applications come to mind in a variety of domains, including public sector services, behavioral health, insurance, risk management, and fraud detection.
Several US states either have implemented or intend to implement predictive models designed to help child support enforcement officers identify noncustodial parents at risk of falling behind on their child support payments. The application is considered a success, but perhaps more could be done to achieve the ultimate goal of ensuring timely child support payments.
In addition, behavioral nudge principles could be used to design financial coaching efforts. Taking the logic a step further, the model could also be used to identify more moderate risks, perhaps not in immediate need of live visits, who might benefit from outreach letters. Various nudge tactics could be used in the design of such letters. Evidence from behavioral nudge field experiments in other applications even suggests that printing such letters on colored paper increases the likelihood that they will be read and acted upon.
There is no way of knowing in advance which if any combination of tactics would prove effective. Nudge tactics could help the case worker most effectively prompt the desired behavior change. Similar ideas can motivate next-generation statistical fraud detection efforts. Fraud detection is among the most difficult data analytics applications because among other reasons it is often the case that not all instances of fraud have been flagged as such in historical databases. Furthermore, fraud itself can be an inherently ambiguous concept. For example, much automobile insurance fraud takes the form of opportunistic embellishment or exaggeration rather than premeditated schemes.
Acting upon a fraud suspicion score can therefore be a subtler task than acting on, for example, child welfare or safety inspection predictive model indications. Judiciously worded letters could be crafted to achieve a variety of fraud mitigation effects. Such an approach is consistent with two well-established psychological facts: People are averse to loss, and they tend to exaggerate small probabilities, particularly when the size of the associated gain or loss is large and comes easily to mind.
For example, social norms exert material influences on our behaviors: People tend to use less energy and fewer hotel towels when informed that people like them tend to conserve. Physician report cards have been found to promote patient safety because they prompt physicians to compare their professional conduct to that of their peers and trigger such internal, noneconomic rewards as professional pride and the pleasure of helping others.
Keeping health care utilization under control is a major goal of both predictive analytics and behavioral economics. Once again, behavioral economics is the natural framework to scientifically attack the last-mile problem of going from predictive model indication to the desired action. In contrast with the building inspection and fraud detection examples, it is unlikely that purely economic incentives are sufficient to change the behavior of the very worst risks.
In this context, the worst risks are those with multiple chronic diseases. Such coaches are selected based more on attitudes and cultural affinities with the patient than on medical training. Indeed, this suggests a further data science application: Use the sort of analytical approaches employed by online matchmaking services to hire and match would-be health coaches to patients based on personality and cultural characteristics.
Gawande recounts the story of a diabetic, obese woman who had suffered three heart attacks. After working with a health coach, she managed to leave her wheelchair and began attending yoga classes. Closely analogous ideas can be pursued in such arenas as financial health and back-to-work programs. The health check was a one-hour coaching session designed to address such behavioral bottlenecks as forgetfulness, burdensome paperwork, lack of self-control, and letting short-term pleasures trump long-term goals.
As with the child support case study, this would enable prevention as well as remediation. But behavioral science is needed to identify the most effective ways to provide the coaching. The discussion so far has focused on behavioral nudge tactics as a way of bridging the gap between predictive model indications and the appropriate actions. Behavioral economics supplies some of the design thinking needed for such innovations. The following examples illustrate this idea.
Viewed through a traditional lens, the telematics black-box devices increasingly used by insurance companies are the ultimate actuarial segmentation machine. They can capture fine-grained details about how much or abruptly we drive, accelerate, change lanes, turn corners, and so on. But in our digitally mediated world of big data, it is possible to consider new business models and product offerings. Such reports could help risky drivers better understand and hopefully improve their behavior, help beginner drivers learn and improve, and help older drivers safely remain behind the wheel longer.
It is worth noting that employing nudge tactics does not preclude using classical economic incentives to promote safer driving. For example, the UK insurer ingenie uses black-box data to calculate risk scores, which in turn are reported via mobile apps back to drivers so that they can better understand and improve their driving behavior.
Self-tracking devices are the health and wellness equivalent of telematics black boxes. Perhaps we can take the behavioral design thinking a step further. Behavioral economics offers a somewhat less dramatic device: the commitment contract. Commitment contracts digitally implemented on mobile app devices offer an intriguing application for self-tracking devices that goes beyond real-time monitoring or feedback reports.
Data from such devices could automatically flow into apps preprogramed with commitment contracts that reflect diet, exercise, or savings goals. But Wilde wrote long before behavioral economics. I yield to the temptation to give one final example of data-fueled, digitally implemented, and behaviorally designed innovation. A striking finding of evidence-based medicine is that nearly , people die each year in the United States alone from preventable hospital infections. A large number of lives could therefore be saved by prompting health care workers to wash their hands for the prescribed length of time.
The DebMed Group Monitoring System is an electronic soap dispenser equipped with a computer chip that records how often the members of different hospital wards wash their hands. Administered with the appropriate behavioral design thinking, an ounce of data can be worth a pound of cure. On the other hand, behavioral nudge applications are often one-size-fits-all affairs applied to entire populations rather than analytically identified sub-segments. It is reasonable to anticipate better results when the two approaches are treated as complementary and applied in tandem. Such data are controversial for reasons not limited to a basic concern for privacy.
Behavioral data generated in one context can be repurposed for use in other contexts to infer preferences, attitudes, and psychological traits with accuracy that many find unsettling at best, Orwellian at worst. These concerns are crucial and not to be swept under the carpet. At the same time, it is possible to view both behavioral data and behavioral nudge science as tools that can be used in either socially useful or socially useless ways. Reward system design and group creativity: An experimental investigation. Chen, H. Ham and N. Designing multiperson tournaments with asymmetric contestants: An experimental study.
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The influence of accountability on overconfidence and resistance to change: A research framework and experimental evidence. Management Accounting Research December : Johnson, E. An accounting procedure for expenditures on experimental projects. Bulletin July 1 : Jones, G. An experimental examination of the effects of individual situational factors on unethical behavioral intentions in the workplace.
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